How does the visual system exploit statistical structure in the world to recognize objects? Here we examine how the distribution of features within objects alters perceptual discrimination of those objects. Object stimuli were plaid textures comprised of two overlapping sinusoidal gratings whose orientations varied independently over non-overlapping ranges (i.e., 1-89° from vertical in opposing directions). On each trial, two of these compound textures were presented and masked sequentially. Observers judged whether they were the same or different (defined by a ±15° rotation in one grating). Experiment 1 explored how the distribution of feature values affected discrimination performance. In an initial exposure phase, one grating (Uniform) was equally likely to appear in any orientation within its range, whereas the orientation of the other grating (Unimodal) was drawn from a truncated Gaussian distribution (peak=30° or 60°, across observers; s.d.=10°). In a subsequent test phase with the same task, both gratings were uniformly distributed, allowing us to test how learning of the distribution affected performance. Although now distributed identically, accuracy was impaired for the (previously) Unimodal vs. Uniform grating. These results are consistent with the possibility that this unimodal distribution promoted narrow tuning for that feature, resulting in impaired representations of objects containing atypical feature values. Experiment 2 examined how this impairment generalizes to more complex feature distributions. The orientation of one grating (Bimodal) was sampled from a mixture of two truncated Gaussians (peaks=30° and 60°); the other grating was Uniform. Again, we found that switching to fully uniform distributions at test impaired discrimination for the (previously) Bimodal vs. Uniform grating. This is consistent with tuning for multiple distinct modes, which resulted in less accurate object discrimination when features were drawn from a different distribution. Taken together, these findings illustrate how the distributional properties of features can have lasting effects on object recognition.